





import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


UpperCAmelCase__   : Optional[int]         =       Mapping[str, np.ndarray]
UpperCAmelCase__   : Tuple         =       Mapping[str, Any]  # Is a nested dict.
UpperCAmelCase__   : int         =       0.01






@dataclasses.dataclass(frozen=snake_case__       )
class 							__lowercase			:
      __UpperCAmelCase		      =						42  # [num_res, num_atom_type, 3]

      # Amino-acid type for each residue represented as an integer between 0 and
      # 20, where 20 is 'X'.
      __UpperCAmelCase		      =						42  # [num_res]

      # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
      # is present and 0.0 if not. This should be used for loss masking.
      __UpperCAmelCase		      =						42  # [num_res, num_atom_type]

      # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
      __UpperCAmelCase		      =						42  # [num_res]

      # B-factors, or temperature factors, of each residue (in sq. angstroms units),
      # representing the displacement of the residue from its ground truth mean
      # value.
      __UpperCAmelCase		      =						42  # [num_res, num_atom_type]

      # Chain indices for multi-chain predictions
      __UpperCAmelCase		      =						None

      # Optional remark about the protein. Included as a comment in output PDB
      # files
      __UpperCAmelCase		      =						None

      # Templates used to generate this protein (prediction-only)
      __UpperCAmelCase		      =						None

      # Chain corresponding to each parent
      __UpperCAmelCase		      =						None






def  A							(			snake_case__   :						str				)					->				Protein:
 '''simple docstring'''
 __snake_case         =			r'(\[[A-Z]+\]\n)'
 __snake_case         =			[tag.strip() for tag in re.split(UpperCamelCase_ ,				UpperCamelCase_				) if len(UpperCamelCase_				) > 0]
 __snake_case         =			zip(tags[0::2] ,				[l.split('\n'				) for l in tags[1::2]]				)

 __snake_case         =			['N', 'CA', 'C']
 __snake_case         =			None
 __snake_case         =			None
 __snake_case         =			None
 for g in groups:
  if "[PRIMARY]" == g[0]:
   __snake_case         =			g[1][0].strip()
   for i in range(len(UpperCamelCase_				)				):
    if seq[i] not in residue_constants.restypes:
     __snake_case         =			'X'  # FIXME: strings are immutable
   __snake_case         =			np.array(
       [residue_constants.restype_order.get(UpperCamelCase_ ,				residue_constants.restype_num				) for res_symbol in seq]				)
  elif "[TERTIARY]" == g[0]:
   __snake_case         =			[]
   for axis in range(3				):
    tertiary.append(list(map(UpperCamelCase_ ,				g[1][axis].split()				)				)				)
   __snake_case         =			np.array(UpperCamelCase_				)
   __snake_case         =			np.zeros((len(tertiary[0]				) // 3, residue_constants.atom_type_num, 3)				).astype(np.floataa				)
   for i, atom in enumerate(UpperCamelCase_				):
    __snake_case         =			np.transpose(tertiary_np[:, i::3]				)
   atom_positions *= PICO_TO_ANGSTROM
  elif "[MASK]" == g[0]:
   __snake_case         =			np.array(list(map({'-': 0, '+': 1}.get ,				g[1][0].strip()				)				)				)
   __snake_case         =			np.zeros(
       (
           len(UpperCamelCase_				),
           residue_constants.atom_type_num,
       )				).astype(np.floataa				)
   for i, atom in enumerate(UpperCamelCase_				):
    __snake_case         =			1
   atom_mask *= mask[..., None]

 assert aatype is not None

 return Protein(
     atom_positions=UpperCamelCase_ ,				atom_mask=UpperCamelCase_ ,				aatype=UpperCamelCase_ ,				residue_index=np.arange(len(UpperCamelCase_				)				) ,				b_factors=UpperCamelCase_ ,				)


def  A							(			snake_case__   :						Protein ,				snake_case__   :						int = 0				)					->				List[str]:
 '''simple docstring'''
 __snake_case         =			[]

 __snake_case         =			prot.remark
 if remark is not None:
  pdb_headers.append(f"REMARK {remark}"				)

 __snake_case         =			prot.parents
 __snake_case         =			prot.parents_chain_index
 if parents is not None and parents_chain_index is not None:
  __snake_case         =			[p for i, p in zip(UpperCamelCase_ ,				UpperCamelCase_				) if i == chain_id]

 if parents is None or len(UpperCamelCase_				) == 0:
  __snake_case         =			['N/A']

 pdb_headers.append(f"PARENT {' '.join(UpperCamelCase_				)}"				)

 return pdb_headers


def  A							(			snake_case__   :						Protein ,				snake_case__   :						str				)					->				str:
 '''simple docstring'''
 __snake_case         =			[]
 __snake_case         =			pdb_str.split('\n'				)

 __snake_case         =			prot.remark
 if remark is not None:
  out_pdb_lines.append(f"REMARK {remark}"				)

 __snake_case         =			42
 if prot.parents is not None and len(prot.parents				) > 0:
  __snake_case         =			[]
  if prot.parents_chain_index is not None:
   __snake_case         =			{}
   for p, i in zip(prot.parents ,				prot.parents_chain_index				):
    parent_dict.setdefault(str(UpperCamelCase_				) ,				[]				)
    parent_dict[str(UpperCamelCase_				)].append(UpperCamelCase_				)

   __snake_case         =			max([int(UpperCamelCase_				) for chain_idx in parent_dict]				)
   for i in range(max_idx + 1				):
    __snake_case         =			parent_dict.get(str(UpperCamelCase_				) ,				['N/A']				)
    parents_per_chain.append(UpperCamelCase_				)
  else:
   parents_per_chain.append(list(prot.parents				)				)
 else:
  __snake_case         =			[['N/A']]

 def make_parent_line(snake_case__   :						Sequence[str]				) -> str:
  return f"PARENT {' '.join(UpperCamelCase_				)}"

 out_pdb_lines.append(make_parent_line(parents_per_chain[0]				)				)

 __snake_case         =			0
 for i, l in enumerate(UpperCamelCase_				):
  if "PARENT" not in l and "REMARK" not in l:
   out_pdb_lines.append(UpperCamelCase_				)
  if "TER" in l and "END" not in lines[i + 1]:
   chain_counter += 1
   if not chain_counter >= len(UpperCamelCase_				):
    __snake_case         =			parents_per_chain[chain_counter]
   else:
    __snake_case         =			['N/A']

   out_pdb_lines.append(make_parent_line(UpperCamelCase_				)				)

 return "\n".join(UpperCamelCase_				)


def  A							(			snake_case__   :						Protein				)					->				str:
 '''simple docstring'''
 __snake_case         =			residue_constants.restypes + ['X']

 def res_atoa(snake_case__   :						int				) -> str:
  return residue_constants.restype_atoa.get(restypes[r] ,				'UNK'				)

 __snake_case         =			residue_constants.atom_types

 __snake_case         =			[]

 __snake_case         =			prot.atom_mask
 __snake_case         =			prot.aatype
 __snake_case         =			prot.atom_positions
 __snake_case         =			prot.residue_index.astype(np.intaa				)
 __snake_case         =			prot.b_factors
 __snake_case         =			prot.chain_index

 if np.any(aatype > residue_constants.restype_num				):
  raise ValueError('Invalid aatypes.'				)

 __snake_case         =			get_pdb_headers(UpperCamelCase_				)
 if len(UpperCamelCase_				) > 0:
  pdb_lines.extend(UpperCamelCase_				)

 __snake_case         =			aatype.shape[0]
 __snake_case         =			1
 __snake_case         =			0
 __snake_case         =			string.ascii_uppercase
 __snake_case         =			None
 # Add all atom sites.
 for i in range(UpperCamelCase_				):
  __snake_case         =			res_atoa(aatype[i]				)
  for atom_name, pos, mask, b_factor in zip(UpperCamelCase_ ,				atom_positions[i] ,				atom_mask[i] ,				b_factors[i]				):
   if mask < 0.5:
    continue

   __snake_case         =			'ATOM'
   __snake_case         =			atom_name if len(UpperCamelCase_				) == 4 else f" {atom_name}"
   __snake_case         =			''
   __snake_case         =			''
   __snake_case         =			1.00
   __snake_case         =			atom_name[0]  # Protein supports only C, N, O, S, this works.
   __snake_case         =			''

   __snake_case         =			'A'
   if chain_index is not None:
    __snake_case         =			chain_tags[chain_index[i]]

   # PDB is a columnar format, every space matters here!
   __snake_case         =			(
       f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
       f"{res_name_a:>3} {chain_tag:>1}"
       f"{residue_index[i]:>4}{insertion_code:>1}   "
       f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
       f"{occupancy:>6.2f}{b_factor:>6.2f}          "
       f"{element:>2}{charge:>2}"
   )
   pdb_lines.append(UpperCamelCase_				)
   atom_index += 1

  __snake_case         =			i == n - 1
  if chain_index is not None:
   if i != n - 1 and chain_index[i + 1] != prev_chain_index:
    __snake_case         =			True
    __snake_case         =			chain_index[i + 1]

  if should_terminate:
   # Close the chain.
   __snake_case         =			'TER'
   __snake_case         =			(
       f"{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]				):>3} {chain_tag:>1}{residue_index[i]:>4}"
   )
   pdb_lines.append(UpperCamelCase_				)
   atom_index += 1

   if i != n - 1:
    # "prev" is a misnomer here. This happens at the beginning of
    # each new chain.
    pdb_lines.extend(get_pdb_headers(UpperCamelCase_ ,				UpperCamelCase_				)				)

 pdb_lines.append('END'				)
 pdb_lines.append(''				)
 return "\n".join(UpperCamelCase_				)


def  A							(			snake_case__   :						Protein				)					->				np.ndarray:
 '''simple docstring'''
 return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def  A							(			snake_case__   :						FeatureDict ,				snake_case__   :						ModelOutput ,				snake_case__   :						Optional[np.ndarray] = None ,				snake_case__   :						Optional[np.ndarray] = None ,				snake_case__   :						Optional[str] = None ,				snake_case__   :						Optional[Sequence[str]] = None ,				snake_case__   :						Optional[Sequence[int]] = None ,				)					->				Protein:
 '''simple docstring'''
 return Protein(
     aatype=features['aatype'] ,				atom_positions=result['final_atom_positions'] ,				atom_mask=result['final_atom_mask'] ,				residue_index=features['residue_index'] + 1 ,				b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask']				) ,				chain_index=UpperCamelCase_ ,				remark=UpperCamelCase_ ,				parents=UpperCamelCase_ ,				parents_chain_index=UpperCamelCase_ ,				)






'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.get_logger(__name__)

enable_full_determinism()

class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Dict									=      UNetaDModel
			a_	: List[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     ={
					    'block_out_channels': (32, 64),
					    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
					    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
					    'attention_head_dim': 3,
					    'out_channels': 3,
					    'in_channels': 3,
					    'layers_per_block': 2,
					    'sample_size': 32,
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Union[str, Any]									=      UNetaDModel
			a_	: Optional[Any]									=      '''sample'''






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =4
					snake_case__	     =4
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10]						).to(_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (4, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Dict:
					return (4, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'sample_size': 32,
					    'in_channels': 4,
					    'out_channels': 4,
					    'layers_per_block': 2,
					    'block_out_channels': (32, 64),
					    'attention_head_dim': 32,
					    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
					    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)

					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						).sample

					assert image is not None, "Make sure output is not None"






			@unittest.skipIf(torch_device != 'cuda'					,   'This test is supposed to run on GPU'						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase						)
					model_accelerate.to(_UpperCAmelCase						)
					model_accelerate.eval()

					snake_case__	     =torch.randn(
					    1					,   model_accelerate.config.in_channels					,   model_accelerate.config.sample_size					,   model_accelerate.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					snake_case__	     =model_accelerate(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					# two models don't need to stay in the device at the same time
					del model_accelerate
					torch.cuda.empty_cache()
					gc.collect()

					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained(
					    'fusing/unet-ldm-dummy-update'					,   output_loading_info=_UpperCAmelCase					,   low_cpu_mem_usage=_UpperCAmelCase						)
					model_normal_load.to(_UpperCAmelCase						)
					model_normal_load.eval()
					snake_case__	     =model_normal_load(_UpperCAmelCase					,   _UpperCAmelCase						)['sample']

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'						)
					model.eval()
					model.to(_UpperCAmelCase						)

					snake_case__	     =torch.randn(
					    1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =noise.to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor([10] * noise.shape[0]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -1, -3:, -3:].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-3						)						)





class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: List[str]									=      UNetaDModel
			a_	: Optional[int]									=      '''sample'''






			@property
			def 						_lowercase  (     self					,   _UpperCAmelCase=(32, 32)						)							->							Tuple:
					snake_case__	     =4
					snake_case__	     =3

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [10]						).to(dtype=torch.intaa					,   device=_UpperCAmelCase						)

					return {"sample": noise, "timestep": time_step}






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							Optional[Any]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'block_out_channels': [32, 64, 64, 64],
					    'in_channels': 3,
					    'layers_per_block': 1,
					    'out_channels': 3,
					    'time_embedding_type': 'fourier',
					    'norm_eps': 1E-6,
					    'mid_block_scale_factor': math.sqrt(2.0						),
					    'norm_num_groups': None,
					    'down_block_types': [
					        'SkipDownBlock2D',
					        'AttnSkipDownBlock2D',
					        'SkipDownBlock2D',
					        'SkipDownBlock2D',
					    ],
					    'up_block_types': [
					        'SkipUpBlock2D',
					        'SkipUpBlock2D',
					        'AttnSkipUpBlock2D',
					        'SkipUpBlock2D',
					    ],
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__     ,	snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,   output_loading_info=_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =self.dummy_input
					snake_case__	     =floats_tensor((4, 3) + (256, 256)						).to(_UpperCAmelCase						)
					snake_case__	     =noise
					snake_case__	     =model(**_UpperCAmelCase						)

					assert image is not None, "Make sure output is not None"






			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(256, 256)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'						)
					model.to(_UpperCAmelCase						)

					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =torch.ones((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)
					snake_case__	     =torch.tensor(batch_size * [1E-4]						).to(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   _UpperCAmelCase						).sample

					snake_case__	     =output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					snake_case__	     =torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]						)
					# fmt: on

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					# not required for this model
					pass




